学术报告第202010期

内容摘要：In   bibliometrics, there are two main procedures to explore a research field:   performance analysis and science mapping.Performance analysis aims at   evaluating groups of scientific actors (countries, universities, departments,   researchers) and the impact of their activity on the basis of bibliographic   data. Science mapping aims at displaying the structural and dynamic aspects   of scientific research, delimiting a research field, and quantifying and   visualizing the detected subfields by means of co-word analysis or documents   co-citation analysis. In this talk we present two bibliometric tools that we   have developed in our research laboratory SECABA: H-Classics to develop   performance analysis by based on Highly Cited Papers and SciMAT to develop   science mapping guided by performance bibliometric indicators.

报告人简介：Enrique   Herrera-Viedma is Professor in Computer Science and   Artificial Intelligence in University of Granada (UGR) and currently,   Vice-President for Research and Knowlegde Transfer. His current research   interests include group decision making, consensus models, linguistic   modeling, aggregation, information retrieval, bibliometrics, digital   libraries, web quality evaluation, recommender systems, blockchain and social media. In these topics he   has published more than 300 papers in ISI journals, coordinated more than 25   research projects, and received more than 35.000 citations according to Web   of Science, being his h-index 83 and 97 in Google Scholar (see   https://scholar.google.com/citations?user=g8ZXTuYAAAAJ&hl=es). Dr. Herrera-Viedma   is Vice-President Publications in IEEE SMC Society, IEEE FELLOW and an   Associate Editor of international journals such as the IEEE Trans. On Syst. Man, and Cyb.:   Systems, IEEE Trans. On Fuzzy Systems, IEEE Trans. On Cybernetics, Knowledge   Based Systems, Soft Computing, Fuzzy Optimization and Decision Making,   Applied Soft Computing, Journal of Intelligent and Fuzzy Systems, and   Information Sciences. He is identified by Clarivate Analytics as HIGHLY CITED   RESEARCHER in Computer Science and Engineering from 2014 to 2020 in both   categories, “Computer Science” and “Engineering”.